Multi-dimensional profiling of elderly at-risk for Alzheimer's disease in a differential framework

The utility of EEG in Alzheimer’s disease (AD) research has been demonstrated over several decades in numerous studies. EEG markers have been employed successfully to investigate AD-related alterations in prodromal AD and AD dementia. Preclinical AD is a recent concept and a novel target for clinical research. This project tackles two issues: first, AD prediction at the preclinical sta ge, by exploiting the multimodal INSIGHT-preAD database, acquired at the Pitie-Salpetriere Hospital; second, an automatic AD diagnosis in a differential framework, by exploiting another large-scale EEG database, acquired at Charles-Foix Hospital. In this project, we will investigate AD predictors at preclinical stage, using EEG data of only subjective Memory Complainers in order to establish a cognitive profiling of elderly at-risk. We will also identify EEG markers for AD detection at early stages in a di fferential diagnosis context. The correlation between EEG markers and clinical biomarkers will be also assessed for a better characterization of the retrieved profiles and a better understanding on the severity of the cognitive disorder. The exploited larg e-scale complementary data offer the opportunity to investigate the full spectrum of the AD neuro-degeneration changes in the brain, using a big data approach and multimodal patient profiling based on resting-state EEG markers